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@arunkrishna142
arunkrishna142 committed Nov 9, 2022
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Learn about vigilant mode.
1 parent 85abf53 commit ec2e38ee3f05061c1532242516f136251f29b9ba
Showing 7 changed files with 5,188 additions and 0 deletions.
1,262 Data Collection/Analyze_the_Dataset.ipynb
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933 Data Collection/Data_Visualization.ipynb
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509 Data Collection/Handling_Missing_Values.ipynb
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"cells": [
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"source": [
"## **Handling Missing Values**"
],
"metadata": {
"id": "9GhlkpKTg5nT"
}
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns"
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"outputs": []
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"})(self);\n",
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]
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"metadata": {}
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{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving Admission_Predict.csv to Admission_Predict.csv\n"
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}
]
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{
"cell_type": "code",
"source": [
"data=pd.read_csv('Admission_Predict.csv')\n",
"data.head()"
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"data": {
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" Serial No. GRE Score TOEFL Score University Rating SOP LOR CGPA \\\n",
"0 1 337 118 4 4.5 4.5 9.65 \n",
"1 2 324 107 4 4.0 4.5 8.87 \n",
"2 3 316 104 3 3.0 3.5 8.00 \n",
"3 4 322 110 3 3.5 2.5 8.67 \n",
"4 5 314 103 2 2.0 3.0 8.21 \n",
"\n",
" Research Chance of Admit \n",
"0 1 0.92 \n",
"1 1 0.76 \n",
"2 1 0.72 \n",
"3 1 0.80 \n",
"4 0 0.65 "
],
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},
{
"cell_type": "code",
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"data.isna().sum()"
],
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"outputId": "aa51af9b-7df7-41ea-a321-e1d68f465334"
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"output_type": "execute_result",
"data": {
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"Serial No. 0\n",
"GRE Score 0\n",
"TOEFL Score 0\n",
"University Rating 0\n",
"SOP 0\n",
"LOR 0\n",
"CGPA 0\n",
"Research 0\n",
"Chance of Admit 0\n",
"dtype: int64"
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},
"metadata": {},
"execution_count": 16
}
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}
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}
53 Data Collection/Importing_the_libraries.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "a631f696",
"metadata": {
"id": "a631f696"
},
"source": [
"## Importing the Libraries"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ff215045",
"metadata": {
"id": "ff215045"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns"
]
}
],
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"kernelspec": {
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},
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"nbformat_minor": 5
}
474 Data Collection/Reading_the_Dataset.ipynb
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{
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"## **Reading the Dataset**"
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{
"cell_type": "code",
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"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns"
],
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"id": "jwzuFbXpg-qW"
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{
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"uploaded = files.upload()"
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"//\n",
"// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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"// limitations under the License.\n",
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"scope.google.colab._files = {\n",
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" _uploadFilesContinue,\n",
"};\n",
"})(self);\n",
"</script> "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving Admission_Predict.csv to Admission_Predict.csv\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"data=pd.read_csv('Admission_Predict.csv')\n",
"data.head()"
],
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"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
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"outputId": "6d8d18ee-9864-4d55-de85-7f224e794cc4"
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" Serial No. GRE Score TOEFL Score University Rating SOP LOR CGPA \\\n",
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}
859 Data Collection/Splitting_The_Data_Into_Train_And_Test.ipynb
View file
@@ -0,0 +1,859 @@
{
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"metadata": {
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{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns"
],
"metadata": {
"id": "jwzuFbXpg-qW"
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{
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"base_uri": "https://localhost:8080/",
"height": 74
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"id": "SGxZKSIphEjo",
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"// See the License for the specific language governing permissions and\n",
"// limitations under the License.\n",
"\n",
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"(function(scope) {\n",
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" resolve(e.target.files);\n",
" });\n",
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"\n",
" const cancel = document.createElement('button');\n",
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"\n",
" outputElement.appendChild(li);\n",
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"\n",
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" let position = 0;\n",
" do {\n",
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" const chunk = new Uint8Array(fileData, position, length);\n",
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"\n",
" const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
" yield {\n",
" response: {\n",
" action: 'append',\n",
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" _uploadFiles,\n",
" _uploadFilesContinue,\n",
"};\n",
"})(self);\n",
"</script> "
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},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving Admission_Predict.csv to Admission_Predict.csv\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"data=pd.read_csv('Admission_Predict.csv')\n",
"data.head()"
],
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"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "m_KzUY5ghx5p",
"outputId": "6d8d18ee-9864-4d55-de85-7f224e794cc4"
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"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" Serial No. GRE Score TOEFL Score University Rating SOP LOR CGPA \\\n",
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"1 2 324 107 4 4.0 4.5 8.87 \n",
"2 3 316 104 3 3.0 3.5 8.00 \n",
"3 4 322 110 3 3.5 2.5 8.67 \n",
"4 5 314 103 2 2.0 3.0 8.21 \n",
"\n",
" Research Chance of Admit \n",
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"3 1 0.80 \n",
"4 0 0.65 "
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" <td>2</td>\n",
" <td>324</td>\n",
" <td>107</td>\n",
" <td>4</td>\n",
" <td>4.0</td>\n",
" <td>4.5</td>\n",
" <td>8.87</td>\n",
" <td>1</td>\n",
" <td>0.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>316</td>\n",
" <td>104</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" <td>3.5</td>\n",
" <td>8.00</td>\n",
" <td>1</td>\n",
" <td>0.72</td>\n",
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" <td>2.5</td>\n",
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" <td>1</td>\n",
" <td>0.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>314</td>\n",
" <td>103</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>8.21</td>\n",
" <td>0</td>\n",
" <td>0.65</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6dcb1ad7-3515-4ab8-80b3-7204f5fad401')\"\n",
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" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
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" border-radius: 50%;\n",
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" width: 32px;\n",
" }\n",
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" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
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" }\n",
" </style>\n",
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" document.querySelector('#df-6dcb1ad7-3515-4ab8-80b3-7204f5fad401 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-6dcb1ad7-3515-4ab8-80b3-7204f5fad401');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"source": [
"# Splitting The Data Into Train And Test"
],
"metadata": {
"id": "GGsMhg27zY_s"
}
},
{
"cell_type": "code",
"source": [
"y=data['GRE Score']\n",
"y"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UKST1VZy5gb8",
"outputId": "653cd7de-b543-44c8-e218-45cd87c37aa0"
},
"execution_count": 50,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 337\n",
"1 324\n",
"2 316\n",
"3 322\n",
"4 314\n",
" ... \n",
"395 324\n",
"396 325\n",
"397 330\n",
"398 312\n",
"399 333\n",
"Name: GRE Score, Length: 400, dtype: int64"
]
},
"metadata": {},
"execution_count": 50
}
]
},
{
"cell_type": "code",
"source": [
"x=data.drop(columns=['GRE Score'],axis=1)\n",
"x.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "UHS2t7Bf5ur5",
"outputId": "a0e7d065-5e2c-4c51-97fb-da31658524f8"
},
"execution_count": 51,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Serial No. TOEFL Score University Rating SOP LOR CGPA Research \\\n",
"0 1 118 4 4.5 4.5 9.65 1 \n",
"1 2 107 4 4.0 4.5 8.87 1 \n",
"2 3 104 3 3.0 3.5 8.00 1 \n",
"3 4 110 3 3.5 2.5 8.67 1 \n",
"4 5 103 2 2.0 3.0 8.21 0 \n",
"\n",
" Chance of Admit \n",
"0 0.92 \n",
"1 0.76 \n",
"2 0.72 \n",
"3 0.80 \n",
"4 0.65 "
],
"text/html": [
"\n",
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" vertical-align: middle;\n",
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"</style>\n",
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" <th></th>\n",
" <th>Serial No.</th>\n",
" <th>TOEFL Score</th>\n",
" <th>University Rating</th>\n",
" <th>SOP</th>\n",
" <th>LOR</th>\n",
" <th>CGPA</th>\n",
" <th>Research</th>\n",
" <th>Chance of Admit</th>\n",
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" <td>8.87</td>\n",
" <td>1</td>\n",
" <td>0.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>104</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" <td>3.5</td>\n",
" <td>8.00</td>\n",
" <td>1</td>\n",
" <td>0.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>110</td>\n",
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" <td>3.5</td>\n",
" <td>2.5</td>\n",
" <td>8.67</td>\n",
" <td>1</td>\n",
" <td>0.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>103</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>8.21</td>\n",
" <td>0</td>\n",
" <td>0.65</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-26969420-f767-4204-9c1b-476ff4070101')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
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" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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" </svg>\n",
" </button>\n",
" \n",
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" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-26969420-f767-4204-9c1b-476ff4070101 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-26969420-f767-4204-9c1b-476ff4070101');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 51
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.model_selection import train_test_split \n",
"x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=50)"
],
"metadata": {
"id": "pLAXMwbW5u0k"
},
"execution_count": 52,
"outputs": []
},
{
"cell_type": "code",
"source": [
"x_train.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "K9ZmGL5N50G9",
"outputId": "b679ccc1-c286-4465-bb78-32a0ad4ebd30"
},
"execution_count": 53,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(320, 8)"
]
},
"metadata": {},
"execution_count": 53
}
]
},
{
"cell_type": "code",
"source": [
"x_test.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "p6f5r0YX518o",
"outputId": "c47d4249-c602-4904-d23b-be2a67898630"
},
"execution_count": 54,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(80, 8)"
]
},
"metadata": {},
"execution_count": 54
}
]
},
{
"cell_type": "code",
"source": [
"y_train.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_3y5B9zs54Lg",
"outputId": "3e377c07-20d0-41d2-ec28-a4985a56bd0f"
},
"execution_count": 55,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(320,)"
]
},
"metadata": {},
"execution_count": 55
}
]
},
{
"cell_type": "code",
"source": [
"y_test.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "eyH4qJK156H1",
"outputId": "3ea135f5-7037-4d88-9ec8-730de13f17b3"
},
"execution_count": 56,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(80,)"
]
},
"metadata": {},
"execution_count": 56
}
]
},
{
"cell_type": "code",
"source": [
"print(x_train.shape,x_test.shape)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "K4EKodNS59DO",
"outputId": "eab09d50-a681-4466-d84c-fad7e0fe921a"
},
"execution_count": 57,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(320, 8) (80, 8)\n"
]
}
]
}
]
}
1,098 Data Collection/Splitting_dependent_And_Independent_Columns.ipynb
View file
@@ -0,0 +1,1098 @@
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns"
],
"metadata": {
"id": "jwzuFbXpg-qW"
},
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from google.colab import files\n",
"uploaded = files.upload()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 74
},
"id": "SGxZKSIphEjo",
"outputId": "b014a980-e39e-43c7-a7a0-805368ed05aa"
},
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <input type=\"file\" id=\"files-798fdbaa-cb6a-4a31-ae13-7f051218dfa5\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-798fdbaa-cb6a-4a31-ae13-7f051218dfa5\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script>// Copyright 2017 Google LLC\n",
"//\n",
"// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"// you may not use this file except in compliance with the License.\n",
"// You may obtain a copy of the License at\n",
"//\n",
"// http://www.apache.org/licenses/LICENSE-2.0\n",
"//\n",
"// Unless required by applicable law or agreed to in writing, software\n",
"// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"// See the License for the specific language governing permissions and\n",
"// limitations under the License.\n",
"\n",
"/**\n",
" * @fileoverview Helpers for google.colab Python module.\n",
" */\n",
"(function(scope) {\n",
"function span(text, styleAttributes = {}) {\n",
" const element = document.createElement('span');\n",
" element.textContent = text;\n",
" for (const key of Object.keys(styleAttributes)) {\n",
" element.style[key] = styleAttributes[key];\n",
" }\n",
" return element;\n",
"}\n",
"\n",
"// Max number of bytes which will be uploaded at a time.\n",
"const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
"\n",
"function _uploadFiles(inputId, outputId) {\n",
" const steps = uploadFilesStep(inputId, outputId);\n",
" const outputElement = document.getElementById(outputId);\n",
" // Cache steps on the outputElement to make it available for the next call\n",
" // to uploadFilesContinue from Python.\n",
" outputElement.steps = steps;\n",
"\n",
" return _uploadFilesContinue(outputId);\n",
"}\n",
"\n",
"// This is roughly an async generator (not supported in the browser yet),\n",
"// where there are multiple asynchronous steps and the Python side is going\n",
"// to poll for completion of each step.\n",
"// This uses a Promise to block the python side on completion of each step,\n",
"// then passes the result of the previous step as the input to the next step.\n",
"function _uploadFilesContinue(outputId) {\n",
" const outputElement = document.getElementById(outputId);\n",
" const steps = outputElement.steps;\n",
"\n",
" const next = steps.next(outputElement.lastPromiseValue);\n",
" return Promise.resolve(next.value.promise).then((value) => {\n",
" // Cache the last promise value to make it available to the next\n",
" // step of the generator.\n",
" outputElement.lastPromiseValue = value;\n",
" return next.value.response;\n",
" });\n",
"}\n",
"\n",
"/**\n",
" * Generator function which is called between each async step of the upload\n",
" * process.\n",
" * @param {string} inputId Element ID of the input file picker element.\n",
" * @param {string} outputId Element ID of the output display.\n",
" * @return {!Iterable<!Object>} Iterable of next steps.\n",
" */\n",
"function* uploadFilesStep(inputId, outputId) {\n",
" const inputElement = document.getElementById(inputId);\n",
" inputElement.disabled = false;\n",
"\n",
" const outputElement = document.getElementById(outputId);\n",
" outputElement.innerHTML = '';\n",
"\n",
" const pickedPromise = new Promise((resolve) => {\n",
" inputElement.addEventListener('change', (e) => {\n",
" resolve(e.target.files);\n",
" });\n",
" });\n",
"\n",
" const cancel = document.createElement('button');\n",
" inputElement.parentElement.appendChild(cancel);\n",
" cancel.textContent = 'Cancel upload';\n",
" const cancelPromise = new Promise((resolve) => {\n",
" cancel.onclick = () => {\n",
" resolve(null);\n",
" };\n",
" });\n",
"\n",
" // Wait for the user to pick the files.\n",
" const files = yield {\n",
" promise: Promise.race([pickedPromise, cancelPromise]),\n",
" response: {\n",
" action: 'starting',\n",
" }\n",
" };\n",
"\n",
" cancel.remove();\n",
"\n",
" // Disable the input element since further picks are not allowed.\n",
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" li.append(span(\n",
" `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
" `last modified: ${\n",
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" do {\n",
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" const chunk = new Uint8Array(fileData, position, length);\n",
" position += length;\n",
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" action: 'append',\n",
" file: file.name,\n",
" data: base64,\n",
" },\n",
" };\n",
"\n",
" let percentDone = fileData.byteLength === 0 ?\n",
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"scope.google.colab = scope.google.colab || {};\n",
"scope.google.colab._files = {\n",
" _uploadFiles,\n",
" _uploadFilesContinue,\n",
"};\n",
"})(self);\n",
"</script> "
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},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving Admission_Predict.csv to Admission_Predict.csv\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"data=pd.read_csv('Admission_Predict.csv')\n",
"data.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
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"id": "m_KzUY5ghx5p",
"outputId": "6d8d18ee-9864-4d55-de85-7f224e794cc4"
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"execution_count": 8,
"outputs": [
{
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"data": {
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" Serial No. GRE Score TOEFL Score University Rating SOP LOR CGPA \\\n",
"0 1 337 118 4 4.5 4.5 9.65 \n",
"1 2 324 107 4 4.0 4.5 8.87 \n",
"2 3 316 104 3 3.0 3.5 8.00 \n",
"3 4 322 110 3 3.5 2.5 8.67 \n",
"4 5 314 103 2 2.0 3.0 8.21 \n",
"\n",
" Research Chance of Admit \n",
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"1 1 0.76 \n",
"2 1 0.72 \n",
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"metadata": {},
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]
},
{
"cell_type": "markdown",
"source": [
"# Split the data into dependent and independent variables"
],
"metadata": {
"id": "GGsMhg27zY_s"
}
},
{
"cell_type": "code",
"source": [
"x = data.iloc[:,0:7]"
],
"metadata": {
"id": "GtNyKJD8zdlX"
},
"execution_count": 32,
"outputs": []
},
{
"cell_type": "code",
"source": [
"x.head()"
],
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"height": 206
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"id": "mruh7koczvt8",
"outputId": "0b891998-c078-4640-d316-e178fb6ee877"
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"execution_count": 33,
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{
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" Serial No. GRE Score TOEFL Score University Rating SOP LOR CGPA\n",
"0 1 337 118 4 4.5 4.5 9.65\n",
"1 2 324 107 4 4.0 4.5 8.87\n",
"2 3 316 104 3 3.0 3.5 8.00\n",
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"\n",
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" element.appendChild(docLink);\n",
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},
"metadata": {},
"execution_count": 33
}
]
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{
"cell_type": "code",
"source": [
"y = data.iloc[:,8:9 ]\n",
"y.head()"
],
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"height": 206
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"id": "2_odsWoc0C1B",
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"outputs": [
{
"output_type": "execute_result",
"data": {
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" Chance of Admit \n",
"0 0.92\n",
"1 0.76\n",
"2 0.72\n",
"3 0.80\n",
"4 0.65"
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" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-52ea15dd-fd11-4145-a4b4-ae1736291e32 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-52ea15dd-fd11-4145-a4b4-ae1736291e32');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 44
}
]
},
{
"cell_type": "code",
"source": [
"print(f'x contains: {x.shape[0]} rows and {x.shape[1]} columns')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qbPhSAS-1Cqu",
"outputId": "b787621f-a1c7-4bc5-8f17-7278b6bc6fee"
},
"execution_count": 45,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"x contains: 400 rows and 7 columns\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(f'y contains: {y.shape}')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lcghe_Ui1E5r",
"outputId": "a99de1e6-2c90-453b-85a5-c8df023d85ae"
},
"execution_count": 46,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"y contains: (400, 1)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.preprocessing import MinMaxScaler\n",
"scaler=MinMaxScaler()\n",
"x[x.columns] = scaler.fit_transform(x[x.columns])"
],
"metadata": {
"id": "FVcUZaH_1IXe"
},
"execution_count": 48,
"outputs": []
},
{
"cell_type": "code",
"source": [
"x.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "dA6qEsLz1Mix",
"outputId": "ffcd9f7a-f6c3-4daa-cf35-8a06e6756562"
},
"execution_count": 49,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Serial No. GRE Score TOEFL Score University Rating SOP LOR \\\n",
"0 0.000000 0.94 0.928571 0.75 0.875 0.875 \n",
"1 0.002506 0.68 0.535714 0.75 0.750 0.875 \n",
"2 0.005013 0.52 0.428571 0.50 0.500 0.625 \n",
"3 0.007519 0.64 0.642857 0.50 0.625 0.375 \n",
"4 0.010025 0.48 0.392857 0.25 0.250 0.500 \n",
"\n",
" CGPA \n",
"0 0.913462 \n",
"1 0.663462 \n",
"2 0.384615 \n",
"3 0.599359 \n",
"4 0.451923 "
],
"text/html": [
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" <div class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Serial No.</th>\n",
" <th>GRE Score</th>\n",
" <th>TOEFL Score</th>\n",
" <th>University Rating</th>\n",
" <th>SOP</th>\n",
" <th>LOR</th>\n",
" <th>CGPA</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.000000</td>\n",
" <td>0.94</td>\n",
" <td>0.928571</td>\n",
" <td>0.75</td>\n",
" <td>0.875</td>\n",
" <td>0.875</td>\n",
" <td>0.913462</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.002506</td>\n",
" <td>0.68</td>\n",
" <td>0.535714</td>\n",
" <td>0.75</td>\n",
" <td>0.750</td>\n",
" <td>0.875</td>\n",
" <td>0.663462</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.005013</td>\n",
" <td>0.52</td>\n",
" <td>0.428571</td>\n",
" <td>0.50</td>\n",
" <td>0.500</td>\n",
" <td>0.625</td>\n",
" <td>0.384615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.007519</td>\n",
" <td>0.64</td>\n",
" <td>0.642857</td>\n",
" <td>0.50</td>\n",
" <td>0.625</td>\n",
" <td>0.375</td>\n",
" <td>0.599359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.010025</td>\n",
" <td>0.48</td>\n",
" <td>0.392857</td>\n",
" <td>0.25</td>\n",
" <td>0.250</td>\n",
" <td>0.500</td>\n",
" <td>0.451923</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
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" background-color: #434B5C;\n",
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" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-fc30e198-6999-41f4-8f94-15779d1282ba button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-fc30e198-6999-41f4-8f94-15779d1282ba');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 49
}
]
}
]
}

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@nhmhussain
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